The process of making good decisions is critical for business analysts and managers because they regularly encounter new problems in an environment characterized by risk and uncertainty. A good decision is not the same as a good outcome, which can sometimes be a matter of luck (uncertainty). Conversely, a bad outcome is not necessarily proof of a bad decision.
In this course, we will explore basic analytical principles that can guide an analyst or manager in making complex decisions. A good decision uses sound reasoning and considers all of the relevant information that is available at the time the decision is to be made.
The analytical areas to be covered in this class include:
Introduction to Data Models and Decisions
Data Analysis & Visualization: Descriptive Statistics
Probability and Random Variables, Normal and other distributions
Decision Making Under Uncertainty: Decision Analysis
Statistical Inference
Linear Regression Modeling and Analysis
The learning goals for the course are that each student be able to:
Transform a seemingly complex business decision problem into an underlying analytical structure
Understand the role of uncertainty and risk in the decision-making process
Manage and analyze available data to understand relationships among variables and create predictions or decisions
Understand the trade-offs involved in a decision
Use available computing technology (e.g., spreadsheets) to arrive at actionable solutions
More specifically, be able to use basic statistical models, use regression models for prediction and understanding relationships, and use decision trees to support information-based decision making
Lastly, to focus on applications, examples, and homework problems relevant to management of business Supply Chains.